Take Home Exercise 1

Author

Hulwana

1 Overview

1.1 Getting Started

In the code chunk below, p_load() of pacman package is used to install and load the following R packages into R environment:

  • sf is use for importing and handling geospatial data in R,

  • tidyverse is mainly use for wrangling attribute data in R,

  • tmap will be used to prepare cartographic quality chropleth map,

  • spdep will be used to compute spatial weights, global and local spatial autocorrelation statistics, and

  • funModeling will be used for rapid Exploratory Data Analysis

pacman::p_load(sf, tidyverse, tmap, spdep, readr, dplyr, tidyr,funModeling)

1.2 Importing Geospatial Data

In this in-class data, two geospatial datasets will beused, they are:

  • geo_export

  • nga_admbnda_adm2_osgof_20190417

1.2.1 Importing Geospatial Data

First, we are going to import the water point geospatial data (i.e. geo_export) by using the code chunk below.

wp <- st_read(dsn = "data",
                   layer = "geo_export",
                   crs = 4326) %>%
  filter(clean_coun == "Nigeria")

Things to learn from the code chunk above:

  • st_read() of sf package is used to import geo_export shapefile into R environment and save the imported geospatial data into simple feature data table.

  • filter() of dplyr package is used to extract water point records of Nigeria.

Next, write_rds() of readr package is used to save the extracted sf data table (i.e. wp) into an output file in rds data format. The output file is called wp_nga.rds and it is saved in geodata sub-folder.

write_rds(wp, "data/wp_nga.rds")

1.2.2 Import Nigeria LGA Boundary data

Now, we are going to import the LGA boundary data into R environment by using the code chunk below.

nga <- st_read(dsn = "data",
               layer = "nga_admbnda_adm2_osgof_20190417",
               crs = 4326)

Thing to learn from the code chunk above.

  • st_read() of sf package is used to import nga_admbnda_adm2_osgof_20190417 shapefile into R environment and save the imported geospatial data into simple feature data table.

1.3 Data Wrangling

1.3.1 Recoding NA values into string

In the code chunk below, replace_na() is used to recode all the NA values in status_cle field into Unknown.

wp_nga <- read_rds("data/wp_nga.rds") %>%
  dplyr::mutate(status_cle = 
           replace_na(status_cle, "Unknown"))

1.3.2 EDA

In the code chunk below, freq() of funModeling package is used to display the distribution of status_cle field in wp_nga.

freq(data=wp_nga, 
     input = 'status_cle')

The above bar chart provide a brief understanding that the percentage of water-points that are functional in Nigeria is slightly less than 50%. It is crucial thus to dive deeper to determine if there are significant pattern in areas that do not have functional water-points and if the neighbouring areas can support those areas that face scarcity in water supply.

Observe that there are two categories with similar names (i.e. ‘Non-Functional due to dry season’ and ‘Non functional due to dry season’, we will standardize this by changing that later to ‘Non-Functional due to dry season’. We will also group those water-points which are marked ‘Abandoned’ with those that are grouped under ‘Abandoned/Decommissioned’.

wp_nga$status_cle[wp_nga$status_cle == "Non functional due to dry season"] <- "Non-Functional due to dry season"
wp_nga$status_cle[wp_nga$status_cle == "Abandoned"] <- "Abandoned/Decommissioned"

We rerun the above code to get the following chart

freq(data=wp_nga, 
     input = 'status_cle')

Distribution of water-points by status

1.4 Extracting Water Point Data

In this section, we will extract the water point records by using classes in status_cle field.

1.4.1 Extracting functional water point

In the code chunk below, filter() of dplyr is used to select functional water points.

wpt_functional <- wp_nga %>%
  filter(status_cle %in%
           c("Functional", 
             "Functional but not in use",
             "Functional but needs repair"))
freq(data = wpt_functional,
     input = "status_cle")

1.4.2 Extracting non-functional water point

In the code chunk below, filter() of dplyr is used to select non-functional water points.

wpt_nonfunctional <- wp_nga %>%
  filter(status_cle %in%
           c("Abandoned/Decommissioned", 
             "Non-Functional",
             "Non-Functional due to dry season"))
freq(data=wpt_nonfunctional, 
     input = 'status_cle')

1.4.3 Extracting water point with Unknown class

In the code chunk below, filter() of dplyr is used to select water points with unknown status.

wpt_unknown <- wp_nga %>%
  filter(status_cle == "Unknown")

1.5 Performing Point-in-Polygon Count

nga_wp <- nga %>% 
  mutate(`total wpt` = lengths(
    st_intersects(nga, wp_nga))) %>%
  mutate(`wpt functional` = lengths(
    st_intersects(nga, wpt_functional))) %>%
  mutate(`wpt non-functional` = lengths(
    st_intersects(nga, wpt_nonfunctional))) %>%
  mutate(`wpt unknown` = lengths(
    st_intersects(nga, wpt_unknown)))

1.5 Saving the Analytical Data Table

nga_wp <- nga_wp %>%
  mutate(pct_functional = `wpt functional`/`total wpt`) %>%
  mutate(`pct_non-functional` = `wpt non-functional`/`total wpt`) %>%
  select(3:4, 8:10, 15:23)

Things to learn from the code chunk above:

  • mutate() of dplyr package is used to derive two fields namely pct_functional and pct_non-functional

  • to keep the file size small, select() of dplyr is used to retain only fields 3, 4, 8, 9, 10, 15, 16, 17, 18, 19, 20, 21, 22,and 23. Fields 3, 4, 8, 9, 10, 15, 16 and 17 captures the different level of geo boundaries in Nigeria. The 4 different boundaries can be seen below;

    plot(nga_wp[,c(1,3,5,6)])

  • ADM2_EN: geo-mapping based on local government area (LGA)

  • ADM1_EN: geo-mapping based on state or federal capital territory

  • ADM0_EN: geo-mapping based on country

  • SD_EN: geo-mapping based on senatorial district

Now, that we have the tidy sf data table subsequent analysis. We will save the sf data table into rds format.

write_rds(nga_wp, "data/nga_wp.rds")

1.6 Visualizing the Spatial Distribution of Water Points

1.6.1 Visualizing based on Local Government Area (LGA) by Count

nga_wp <- read_rds("data/nga_wp.rds")
total <- qtm(nga_wp, "total wpt")
wp_functional <- qtm(nga_wp, "wpt functional")
wp_nonfunctional <- qtm(nga_wp, "wpt non-functional")
unknown <- qtm(nga_wp, "wpt unknown")

tmap_mode("view")
tmap_arrange(total, wp_functional, wp_nonfunctional, unknown, 
             asp=1, ncol=2)

Based on the above chart, we briefly observe that in terms of functional waterpoints, the north-west zone has the most functional waterpoints, whereas the number of non-functional water-points seems to be scattered all over in Nigeria.

It is interesting to note that while the district Ifelodun has a relatively higher number of functional waterpoints, it also has the highest number of non-functional waterpoints.

In terms of unknown waterpoint statuses it it mostly populated in the north-central zone of Nigeria.

1.6.2 Visualizing based on Local Government Area (LGA) by Quantile

Notice, that areas with high counts of functional waterpoints or high counts of non-functional waterpoints are rather sparse and the number of areas falling in each bucket of number scale are not evenly distributed. This might be misleading in terms of understanding the waterpoint distribution accross Nigeria and instead we will take a look at the distribution based on the quantile.

We run the code below to get the intended geo-visualization:

tmap_mode("plot")
total <- tm_shape(nga_wp)+ 
  tm_fill("total wpt", style = "quantile") +
  tm_borders()

wp_functional <- tm_shape(nga_wp)+ 
  tm_fill("wpt functional", style = "quantile") +
  tm_borders()
  
wp_nonfunctional <- tm_shape(nga_wp)+ 
  tm_fill("wpt non-functional", style = "quantile") +
  tm_borders()

unknown <- tm_shape(nga_wp)+ 
  tm_fill("wpt unknown", style = "quantile") +
  tm_borders()

tmap_arrange(total, wp_functional, wp_nonfunctional, unknown, 
             asp=1, ncol=2)

Based on the above chart, we see that the above mapping is divided into many subareas. Perhaps we could visualize by a certain district or state.

1.6.3 Visualizing based on State/Federal Capital Territory by Count

To see if the number of functional and non-functional waterpoints are evenly distributed or concentrated to a specific region, we will use the ADM2_EN field to outline the broader area in Nigeria.

We will first have to aggregate the total waterpoints, total functional waterpoints, total non-functional waterpoints and total unknown waterpoints by the respective state using the following code:

nga_state <- nga_wp %>%
  group_by(ADM1_EN) %>%
  summarise(total_wp = sum(`total wpt`),
            total_functional = sum(`wpt functional`),
            total_non_functional = sum(`wpt non-functional`),
            total_unknown = sum(`wpt unknown`))

The following code chunk is executed to obtain the visualization

tmap_mode("plot")

total <- tm_shape(nga_state)+
  tm_fill("total_wp", palette="BuGn") +
  tm_borders()

wp_functional <- tm_shape(nga_state)+
  tm_fill("total_functional", palette="BuGn") +
  tm_borders()

wp_nonfunctional <- tm_shape(nga_state)+
  tm_fill("total_non_functional", palette="BuGn") +
  tm_borders()

unknown <- tm_shape(nga_state)+
  tm_fill("total_unknown", palette="BuGn") +
  tm_borders()

tmap_arrange(total, wp_functional, wp_nonfunctional, unknown,
             asp=1, ncol=2)

In contrast to plotting based on LGA, we see that for non-functional points are more spread based on the plotting via state region.

However, in terms of total waterpoints, total functional waterpoints and total unknown waterpoints have number of areas that are uniformly distributed against the number category, we will proceed to plot the distribution via quantile instead of count.

1.6.4 Visualizing based on State/Federal Capital Territory by Quantile

To visualize the distribution of waterpoints across the different state in Nigeria, we run the following code:

tmap_mode("plot")

total <- tm_shape(nga_state)+
  tm_fill("total_wp", palette="BuGn", style="quantile") +
  tm_borders()

wp_functional <- tm_shape(nga_state)+
  tm_fill("total_functional", palette="BuGn", style="quantile") +
  tm_borders()

wp_nonfunctional <- tm_shape(nga_state)+
  tm_fill("total_non_functional", palette="BuGn", style="quantile") +
  tm_borders()

unknown <- tm_shape(nga_state)+
  tm_fill("total_unknown", palette="BuGn", style="quantile") +
  tm_borders()

tmap_arrange(total, wp_functional, wp_nonfunctional, unknown,
             asp=1, ncol=2)

2 Analysis

2.1 Further transformation

In geospatial analytics, it is very common for us to transform the original data from geographic coordinate system to projected coordinate system. This is because geographic coordinate system is not appropriate if the analysis need to use distance or/and area measurements.

The print below reveals that the assigned coordinates system is WGS 84, the ‘World Geodetic System 1984’ which is inappropriate in our case and should be using the CRS of Nigeria with an ESPG code of either 26391, 26392, and 26303. A country’s epsg code can be obtained by referring to epsg.io.

We will use the EPSG code of 26391 in our analysis.

st_geometry(nga_wp)
Geometry set for 774 features 
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2.668534 ymin: 4.273007 xmax: 14.67882 ymax: 13.89442
Geodetic CRS:  WGS 84
First 5 geometries:

Based on the initial dataset it is in Geodetic CRS and thus we need to reproject nga_wp to another coordinate system mathemetically using the st_transform function of the sf package, as shown by the code chunk below.

nga_wp26391 <- st_transform(nga_wp, crs = 26391)

Next, we will view the content of nga_wp26391 sf data frame as shown below.

st_geometry(nga_wp26391)
Geometry set for 774 features 
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 28879.72 ymin: 30292.37 xmax: 1343798 ymax: 1094244
Projected CRS: Minna / Nigeria West Belt
First 5 geometries:

Notice that instead of Geodetic CRS it has been changed to a Projected CRS of Minna / Nigeria West Belt.

Limitations/ Further work

For future work to consider demarcate the different regions in Nigeria as outline below to understand better if certain region faced water shortage more severely than other regions.